Advertisement

Adaptive Allocation of Data-Objects in the Web Using Neural Networks

  • Joaquin Pérez O.
  • Rodolfo A. Pazos R.
  • Hector J. Fraire H.
  • Laura Cruz R.
  • Johnatan E. Pecero S.
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2829)

Abstract

In this paper we address the problem of allocation scheme design of large database-objects in the Web environment, which may suffer significant changes in usage and access patterns and scaling of data. In these circumstances, if the design is not adjusted to new changes, the system can undergo severe degradations in data access costs and response time. Since this problem is NP-complete, obtaining optimal solutions for large problem instances requires applying approximate methods. We present a mathematical model to generate a new object allocation scheme and propose a new method to solve it. The method uses a Hopfield neural network with the mean field annealing (MFA) variant. The experimental results and a comparative study with other two methods are presented. The new method has a similar capacity to solve large problem instances, regular level of solution quality and excellent execution time with respect to other methods.

Keywords

Tabu Search Reinforcement Learn Allocation Scheme Access Pattern Transmission Cost 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Oszu, M., Valduries, P.: Principles of Distributed Database Systems. Prentice-Hall, Englewood Cliffs (1991)Google Scholar
  2. 2.
    Pérez, J., Pazos, R., Frausto, J., Romero, D., Cruz, L.: Vertical Fragmentation and Allocation in Distributed Databases with Site Capacity Restrictions Using Threshold Accepting Algorithm. In: Cairó, O., Cantú, F.J. (eds.) MICAI 2000. LNCS(LNAI), vol. 1793, pp. 75–81. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  3. 3.
    Pérez, J., Pazos, R., Vélez, L., Rodríguez, G.: Automatic Generation of Control Parameters for the Threshold Accepting Algorithm. In: Coello Coello, C.A., de Albornoz, Á., Sucar, L.E., Battistutti, O.C. (eds.) MICAI 2002. LNCS (LNAI), vol. 2313, pp. 118–127. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Pérez, J., Pazos, R., Romero, D., Santaolaya, R., Rodríguez, G., Sosa, V.: Adaptive and Scalable Allocation of Data-Objects in the Web. In: Kumar, V., Gavrilova, M.L., Tan, C.J.K., L’Ecuyer, P. (eds.) ICCSA 2003. LNCS, vol. 2667, pp. 134–143. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Díaz, A., González, J., Laguna, M., Moscato, P., Tseng, F., Glover, F., Ghaziri, F.: Optimización Heurística y Redes Neuronales. Editorial Paraninfo 235 (1996)Google Scholar
  6. 6.
    Hilera, J., Martínez, V.: Redes Neuronales Artificiales, Fundamentos, Modelos y Aplicaciones. Editorial Alfaomega 390 (1995)Google Scholar
  7. 7.
    Peterson, C., Söderberg, B.: Neural Optimization. The Handbook of Brain Research and Neural Networks. 2nd edn. MIT Press, Cambridge (1995)Google Scholar
  8. 8.
    Smith, K.: Neural Networks for Combinatorial Optimization: A review of More Than a Decade of Research. Journal of Computing 11(1) (1999)Google Scholar
  9. 9.
    Smith, K.: Neural Techniques for Combinatorial Optimization with Applications. IEEE Transactions on Neural Networks 9(6) (1998)Google Scholar
  10. 10.
    Shaharuddin, S., Zomaya, A.: Multiprocessor Scheduling Using Mean Field Annealing, http://ipdps.eece.unm.edu/1998/biosp3/bipp9.pdf
  11. 11.
    Bultan, T., Aykanat, C.: A New Mapping Heuristic Based on Mean Field Annealing. Intel Supercomputer Systems Division and Research CouncilGoogle Scholar
  12. 12.
    Vélez, L.: Esquema de Enfriamiento Adaptivo para el Algoritmo de Aceptación por Umbral Aplicado al Diseño de Bases de Datos Distribuidas. MS Thesis, Instituto Tecnológico de León, León (2000)Google Scholar
  13. 13.
    Ceri, S., Pernici, B., Wiederhold, G.: Distributed Database Design Methodologies. Proc. IEEE 75(5), 533–546 (1987)CrossRefGoogle Scholar
  14. 14.
    Ohlsson, M., Peterson, M., Söderberg, C. B.: Neural Networks for Optimization Problems with Inequality Constraints- the Knapsack Problem. Department of Theorical Physics. University of Lund, SwedenGoogle Scholar
  15. 15.
    Colunga, A., Muñoz, L., Ramírez, M.: Agente Inteligente de Aprendizaje Reforzado Aplicado al Diseño de Bases de Datos Distribuidas. BS Thesis, Instituto Tecnológico de Cd. Madero (2001)Google Scholar
  16. 16.
    González, J., Fraire, H., Moreno, A., Muñoz, L., Ramírez, M.: Agente Inteligente de Aprendizaje Reforzado Aplicado al Diseño de Base de Datos Distribuidas. In: Procs. IEEE, CIECE (2001)Google Scholar
  17. 17.
    Laurence, C.: Evaluación del Modelo DFAR Usando Tabu Search. Technical report, Instituto Tecnológico de Cd. Madero (2001)Google Scholar
  18. 18.
    Pérez, J., Pazos, R.A., Romero, D., Cruz, L.: Análisis de Complejidad del Problema de la Fragmentación Vertical y Reubicación Dinámica en Bases de Datos Distribuidas. In: Proceeding of 7mo. Congreso Internacional de Investigación en Ciencias Computacionales (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Joaquin Pérez O.
    • 1
  • Rodolfo A. Pazos R.
    • 1
  • Hector J. Fraire H.
    • 2
  • Laura Cruz R.
    • 2
  • Johnatan E. Pecero S.
    • 2
  1. 1.National Center of Research and Technology DevelopmentCuernavaca, Mor.México
  2. 2.Ciudad Madero Technology InstituteCd. Madero, Tam.México

Personalised recommendations